Robust Pavement Modulus Prediction Using Time-Structured Deep Models and Perturbation-Based Evaluation on FWD Data
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsPlease see the attachment below.
Comments for author File: 
Comments.pdf
Author Response
Please see the attached PDF for our responses to the reviewers’ feedback.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study deals with the robust pavement modulus prediction using time-structured deep models and perturbation-based evaluation on Falling Weight Deflectometer data. The reviewer has some comments as follows:
1) Compared with the method in this study, other methods for determining the pavement modulus must be described. The advantages and disadvantages of each method must be discussed.
2) The authors need to explain why Gaussian noise was chosen in this study. Is Gaussian noise suitable for real conditions?
3) The study mentioned the experiment. Actual figures of the experiment should be added.
4) For section 3.4.1, each evaluation metric must be stated in terms of its meaning and how to use each metric to evaluate the results.
5) A detailed flowchart (step by step) must be added to help the reader understand the proposed method in the study.
6) For the results in tables 7-10, the error values between the predicted and actual modulus need to be added.
7) The citation of references is full of errors, for example [40,40], [Error! Reference source not found].
8) The manuscript should not have paragraphs that consist of only 2 to 3 sentences.
9) English needs to be polished.
Comments on the Quality of English LanguageEnglish needs to be polished.
Author Response
Please see the attached PDF for our responses to the reviewers’ feedback.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsNo more comments.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe manuscript has been revised according to the reviewer's comments.
